Abstract

Deep reinforcement learning techniques have shown to be a promising path tosolve very complex tasks that once were thought to be out of the realm ofmachines. However, while humans and animals learn incrementally during theirlifetimes and exploit their experience to solve new tasks, standard deeplearning methods specialize to solve only one task at a time and whateverinformation they acquire is hardly reusable in new situations. Given that anyartificial agent would need such a generalization ability to deal with thecomplexities of the world, it is critical to understand what mechanisms giverise to this ability. We argue that one of the mechanisms humans rely on is theuse of discrete conceptual representations to encode their sensory inputs.These representations group similar inputs in such a way that combined theyprovide a level of abstraction that is transverse to a wide variety of tasks,filtering out irrelevant information for their solution. Here, we show that itis possible to learn such concept-like representations by self-supervision,following an information-bottleneck approach, and that these representationsaccelerate the transference of skills by providing a prior that guides thepolicy optimization process. Our method is able to learn useful concepts inlocomotive tasks that significantly reduce the number of optimization stepsrequired, opening a new path to endow artificial agents with generalizationabilities.